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Tytuł artykułu

Optimizing rock fragmentation in open-pit mines through fuzzy intelligent prediction method

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Blasting is one of the most important steps in mining operation and it directly affects final results (extraction ore body and costs). Various parameters such as rock mass and explosive properties, and blast geometry influence blasting results. A number of effective parameters in fragmentation should be taken into account to design a suitable blasting pattern, reduce the secondary costs and minimize the adverse effects such as flyrock, back break and ground vibration. Fuzzy theory is a widely used technique in many engineering subjects in which there exist concepts of quality and uncertainly. In this study, the information obtained from blasting operation in B anomaly Sangan Iron Mines have been used. In this model, the blasting pattern parameters such as burden, spacing, hole depth, stemming, charging length, ratio of (K/B), number of rows, specific charge and charge per delay ratio were considered as the input parameters in fuzzy model. Then, the results of fuzzy model were compared with statistical models. Finally, the results of the two models produced from mine blasting operation were compared and evaluated with real values. The correlation coefficient index for two models were 97.8% and 72.19%, and the RMSE were 2.613 and 9.18, respectively.
Czasopismo
Rocznik
Tom
Strony
21--38
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
autor
  • Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  • Department of Industrial Management, Babol Branch, Islamic Azad University, Babol, Iran
Bibliografia
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  • AKBARI M., LASHKARIPOUR G., BAFGHI A.Y., and GHAFOORI M., 2015, Blastability evaluation for rock mass fragmentation in Iran central iron ore mines, International Journal of Mining Science and Technology, 25 (1), 59–66.
  • ALAVALA C.R., 2008, Fuzzy Logic and Neural Networks: Basic Concepts and Applications, New Age International Publishers, New Delhi.
  • ANDRIEVSKY A.P. and AKHPASHEV B.A., 2017, Improvement of rock fragmentation by distributed charge blasting, Journal of Mining Science, 53 (2), 253–258.
  • AYDIN A., 2004, Fuzzy set approaches to classification of rock masses, Engineering Geology, 74 (3–4), 227–245.
  • AZIMI Y., OSANLOO M., SHIRAZI M.A., and BAZZAZI A.A., 2010, Prediction of the blastability designation of rock masses using fuzzy sets, International Journal of Rock Mechanics and Mining Sciences, 47 (7), 1126–1140.
  • BAHRAMI A., MONJEZI M., GOSHTASBI K., and GHAZVINIAN A., 2011, Prediction of rock fragmentation due to blasting using artificial neural network, Engineering with Computers, 27 (2), 177–181.
  • BAMFORD T., ESMAEILI K., and SCHOELLIG A.P., 2021, A deep learning approach for rock frag-mentation analysis, International Journal of Rock Mechanics and Mining Sciences, 145, 104839.
  • BERTA G., 1990, Explosives: An Engineering Tool, Italesplosivi, Milano.
  • DEHGHANI H. and MONJEZI M., 2008, Evaluation of effect of blasting pattern parameters on back break using neural networks, International Journal of Rock Mechanics and Mining Sciences, 45 (8), 1446–1453.
  • DING X., BAHADORI M., HASANIPANAH M., and ABDULLAH R.A., 2023, Predicting the rock fragmentation in surface mines using optimized radial basis function and cascaded forward neural network models, Geomechanics and Engineering, 33 (6), 567–581.
  • EBRAHIMI E., MONJEZI M., KHALESI M.R., and ARMAGHANI D.J., 2016, Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm, Bulletin of Engineering Geology and the Environment, 75 (1), 27–36.
  • FARAMARZI F., MANSOURI H., and EBRAHIMI FARSANGI M.A., 2013, A rock engineering systems based model to predict rock fragmentation by blasting, International Journal of Rock Mechanics and Mining Sciences, 60, 82–94.
  • GREEN M., BJORK J., FORBERG J., EKELUND U., EDENBRANDT L., and OHLSSON M., 2006, Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room, Artificial Intelligence in Medicine, 38 (3), 305–318.
  • GRIMA M.A., 2000, Neuro-Fuzzy Modeling in Engineering Geology: Applications to mechanical rock excavation, rock strength estimation and geological mapping, CRC Press, Rotterdam.
  • HUSTRULID W., 1999, Blasting Principles for Open Pit Mining, A.A. Balkema, Rotterdam.
  • HUSTRULID W.A., KUCHTA M., and MARTIN R.K., 2013, Open Pit Mine Planning and Design, CRC Press, New York.
  • IPHAR M. and GOKTAN R.M., 2006, An application of fuzzy sets to the Diggability Index Rating Meth-od for surface mine equipment selection, International Journal of Rock Mechanics and Mining Sciences, 43 (2), 253–266.
  • JEON S., KIM T.H., and YOU K.H., 2015, Characteristics of crater formation due to explosives blasting in rock mass, Geomechanics and Engineering, 9 (3), 329–344.
  • JIMENO C.L., JIMENO V.L., and CARCEDO F.G., 1995, Drilling and blasting of rocks, A.A. Balkema, Rotterdam.
  • KIM S., LEE M., and LEE J., 2017, A study of fuzzy membership functions for dependence decision-making in security robot system, Neural Computing and Applications, 28 (1), 155–164.
  • LEE H., JEON S., and KIM J., 2003, Development of a fuzzy model to estimate engineering rock mass properties, 10th ISRM Congress, Sandton, South Africa, pp. 749–752.
  • MEHRABI B., SIANI M.G., ZHANG R., NEUBAUER F., LENTZ D.R., FAZEL E.T., SHAHRAKI B.K., 2021, Mineralogy, petrochronology, geochemistry, and fluid inclusion characteristics of the Dardvay skarn iron deposit, Sangan mining district, NE Iran, Ore Geology Reviews, 134, 104146.
  • MONJEZI M., AMIRI H., FARROKHI A., and GOSHTASBI K., 2010b, Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks, Geotechnical and Geological Engineering, 28 (4), 423–430.
  • MONJEZI M., BAHRAMI A., and VARJANI A.Y., 2010a, Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks, International Journal of Rock Mechanics and Mining Sciences, 47 (3), 476–480.
  • MONJEZI M., REZAEI M., and YAZDIAN A., 2010c, Prediction of backbreak in open-pit blasting using fuzzy set theory, Expert Systems with Applications, 37 (3), 2637–2643.
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  • ORAEE K. and ASI B., 2006, Prediction of Rock Fragmentation in Open Pit Mines, using Neural Net-work Analysis, Fifteenth International Symposium on Mine Planning and Equipment Selection (MPES), Turin, Italy.
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  • ROSS T.J., 2010, Fuzzy Logic with Engineering Application, 3rd Ed., John Wiley & Sons, Ltd., West Sussex, United Kingdom.
  • SANCHIDRIÁN J.A. and OUCHTERLONY F., 2017, A Distribution-Free Description of Fragmentation by Blasting Based on Dimensional Analysis, Rock Mechanics and Rock Engineering, 50 (4), 781–806.
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  • YAGIZ S. and GOKCEOGLU C., 2010, Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness, Expert Systems with Applications, 37 (3), 2265–2272.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9e69b21e-9627-4dcb-9182-55eb9f47c918
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